Brian has several different methods for running the computations in a
simulation. The default mode is Runtime code generation, which runs the simulation loop
in Python but compiles and executes the modules doing the actual simulation
work (numerical integration, synaptic propagation, etc.) in a defined target
language. Brian will select the best available target language automatically.
On Windows, to ensure that you get the advantages of compiled code, read
the instructions on installing a suitable compiler in
Windows.
Runtime mode has the advantage that you can combine the computations
performed by Brian with arbitrary Python code specified as NetworkOperation.

The fact that the simulation is run in Python means that there is a (potentially
big) overhead for each simulated time step. An alternative is to run Brian in with
Standalone code generation – this is in general faster (for certain types of simulations
much faster) but cannot be used for all kinds of simulations. To enable this
mode, add the following line after your Brian import, but before your simulation
code:

set_device('cpp_standalone')

For detailed control over the compilation process (both for runtime and standalone
code generation), you can change the Compiler settings that are used.

Code generation means that Brian takes the Python code and strings
in your model and generates code in one of several possible different
languages and actually executes that. The target language for this code
generation process is set in the codegen.target preference. By default, this
preference is set to 'auto', meaning that it will chose a compiled language
target if possible and fall back to Python otherwise (it will also raise a warning
in this case, set codegen.target to 'numpy' explicitly to avoid this warning).
There are two compiled language targets for Python 2.x, 'weave' (needing a
working installation of a C++ compiler) and 'cython' (needing the Cython
package in addition); for Python 3.x, only 'cython' is available. If you want to
chose a code generation target explicitly (e.g. because you want to get rid of the
warning that only the Python fallback is available), set the preference to 'numpy',
'weave' or 'cython' at the beginning of your script:

frombrian2import*prefs.codegen.target='numpy'# use the Python fallback

Do not use the weave code generation targets when running multiple
simulations in parallel. See Known issues for more
details.

You might find that running simulations in weave or Cython modes won’t work
or is not as efficient as you were expecting. This is probably because you’re
using Python functions which are not compatible with weave or Cython. For
example, if you wrote something like this it would not be efficient:

The reason is that the function f(x) is a Python function and so cannot
be called from C++ directly. To solve this problem, you need to provide an
implementation of the function in the target language. See
Functions.

Brian supports generating standalone code for multiple devices. In this mode, running a Brian script generates
source code in a project tree for the target device/language. This code can then be compiled and run on the device,
and modified if needed. At the moment, the only “device” supported is standalone C++ code.
In some cases, the speed gains can be impressive, in particular for smaller networks with complicated spike
propagation rules (such as STDP).

To use the C++ standalone mode, you only have to make very small changes to your script. The exact change depends on
whether your script has only a single run() (or Network.run()) call, or several of them:

At the beginning of the script, i.e. after the import statements, add:

set_device('cpp_standalone')

The CPPStandaloneDevice.build function will be automatically called with default arguments right after the run()
call. If you need non-standard arguments then you can specify them as part of the set_device() call:

The build function has several arguments to specify the output directory, whether or not to
compile and run the project after creating it and whether or not to compile it with debugging support or not.

To run multiple full simulations (i.e. multiple device.build calls, not just
multiple run() calls as discussed above), you have to reinitialize the device
again:

device.reinit()device.activate()

Note that the device “forgets” about all previously set build options provided
to set_device() (most importantly the build_on_run option, but also e.g. the
directory), you’ll have to specify them as part of the Device.activate call.
Also, Device.activate will reset the defaultclock, you’ll therefore have to
set its dtafter the activate call if you want to use a non-default
value.

Not all features of Brian will work with C++ standalone, in particular Python based network operations and
some array based syntax such as S.w[0,:]=... will not work. If possible, rewrite these using string
based syntax and they should work. Also note that since the Python code actually runs as normal, code that does
something like this may not behave as you would like:

results=[]forvalinvals:# set up a networkrun()results.append(result)

The current C++ standalone code generation only works for a fixed number of run statements, not with loops.
If you need to do loops or other features not supported automatically, you can do so by inspecting the generated
C++ source code and modifying it, or by inserting code directly into the main loop as follows:

After a simulation has been run (after the run() call if set_device() has been called with build_on_run set to
True or after the Device.build call with run set to True), state variables and
monitored variables can be accessed using standard syntax, with a few exceptions (e.g. string expressions for indexing).

When using the C++ standalone mode, you have the opportunity to turn on multi-threading, if your C++ compiler is compatible with
OpenMP. By default, this option is turned off and only one thread is used. However, by changing the preferences of the codegen.cpp_standalone
object, you can turn it on. To do so, just add the following line in your python script:

prefs.devices.cpp_standalone.openmp_threads=XX

XX should be a positive value representing the number of threads that will be
used during the simulation. Note that the speedup will strongly depend on the
network, so there is no guarantee that the speedup will be linear as a function
of the number of threads. However, this is working fine for networks with not
too small timestep (dt > 0.1ms), and results do not depend on the number of
threads used in the simulation.

If using C++ code generation (either via weave, cython or standalone), the
compiler settings can make a big difference for the speed of the simulation.
By default, Brian uses a set of compiler settings that switches on various
optimizations and compiles for running on the same architecture where the
code is compiled. This allows the compiler to make use of as many advanced
instructions as possible, but reduces portability of the generated executable
(which is not usually an issue).